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Paper Citation Record · LEDGER

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling

As of 22 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 0 inbound Pith citation observations for arXiv:2605.23198.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2605.23198 v1

Coverage vector

measured 65 of 65 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-25T05:24:53.628214Z

measured 65 of 65 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

65 of 65 outbound references displayed

  • verified exact3
  • verified fuzzy55
  • unresolved3
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch4

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ddad9a06-dd4d-4325-bca3-e264c0ee9aaf · outbound

This paper cites Deep Learning Scaling is Predictable, Empirically.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling Deep Learning Scaling is Predictable, Empirically

Reference 1

Resolution
metadata mismatch
local_arxiv, observed 2026-05-25T05:25:23.280458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:e7ea2492a85186bd35d279abc49697242a3e79a1ffef71cd5795eb114f91efe8

Observation 1eed6c6e-6346-4ee3-a50f-e5f29c556bf9 · outbound

This paper cites A Constructive Prediction of the Generalization Error Across Scales.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling A Constructive Prediction of the Generalization Error Across Scales

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-25T05:25:23.285502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:bb087efbd4e8f5fd8ff9d971b49df76b8516ebdb45edc2aa3c467a31261750a0

Observation 019ecc54-6c2b-4861-b8fa-43891ce8ee86 · outbound

This paper cites Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , pages=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , pages=

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.716769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:f23af9e026f76ec4c9b7f4216e3f2df32bb44b386b561d261640590ed6811640

Observation 8f2f3347-ade1-421b-9ff4-ea78c9453b8f · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling Advances in Neural Information Processing Systems , volume=

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.710765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:24374aa82d962c9a9d201f0df909586e3cb8e650b629cf7b6fd734bb6ae3212f

Observation 9a7aa55b-3ba2-4ab6-923d-d837ba2639f9 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling LLaMA: Open and Efficient Foundation Language Models

Reference 5

Resolution
metadata mismatch
local_arxiv, observed 2026-05-25T05:25:23.271235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:3fd770bb449c58cded0fec4d7ffa13a7bcff8b62b9eebc7752066f2e05e72654

Observation 8250142d-1c84-4c11-9f39-8a2d8b7dc164 · outbound

This paper cites GPT-4 Technical Report.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling GPT-4 Technical Report

Reference 6

Resolution
metadata mismatch
local_arxiv, observed 2026-05-25T05:25:23.275749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:f3c58f5751c08816168a8a904580d2f179a1c23e1376675480548398c7efaf71

Observation e0bd35ad-7b68-4e12-ac61-b1c45ececf4b · outbound

This paper cites International Conference on Learning Representations , year=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling International Conference on Learning Representations , year=

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.695503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:55cfda8663434894fb971380e818aa0c01f68ec79adc0ded52ecc15da6a2af92

Observation 7b0d7a60-bd11-492c-9f24-9f8a8a3cb8ac · outbound

This paper cites International Conference on Learning Representations , year=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling International Conference on Learning Representations , year=

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.704599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:de5d4021cfd39b334c119bced65ce25877605345d8ac234120c7c6fca913994c

Observation 92324043-5bce-4842-bf53-3eca700a3e90 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling Advances in Neural Information Processing Systems , volume=

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.707774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:4dcf8a7141141a0f4ae9eb99b8bea12065be76abb0c731f40b88aabd06a10faf

Observation d0c68588-5c3b-4f9e-909f-c5df6dfcaee9 · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.720119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:42f570d12fe9120127f6d94d0592603e816e24ed0b9addc816fe7fdf5fc68c21

Observation a5c236be-ae14-48ea-89e5-4649820b9558 · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.701565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:82d0e43531ab74459760ec244944444073fa4d0a88e9789d628eba63ee18ce05

Observation 15785be3-b25f-4c4c-b2d8-f980acc7c228 · outbound

This paper cites Advances in neural information processing systems , volume=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling Advances in neural information processing systems , volume=

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.726074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:3232f465c8817cee45209b9d9a9629e758d3c527309953ecdb8e99d1673cdf4b

Observation b9940404-7c2c-4c02-a6f7-608f73e2ccf3 · outbound

This paper cites The Eleventh International Conference on Learning Representations , year=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling The Eleventh International Conference on Learning Representations , year=

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.689279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:dfc0546c698110317486280210909c34db9c853114a5543d08bd3858c930103e

Observation fa77b330-9594-434b-a7e0-a508e719bad1 · outbound

This paper cites an unresolved cited work.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling Unresolved cited work

Reference 14

Resolution
unresolved
raw_fallback, observed 2026-05-25T11:50:46.713795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:8be1219aadfd1ec305f1b905fed3fb47a0f6916c7ef3dbe7d3c5c0b61fb6ba55

Observation 09046c39-c7bb-4b47-a30e-a7fa35f9bdb7 · outbound

This paper cites The Eleventh International Conference on Learning Representations , year=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling The Eleventh International Conference on Learning Representations , year=

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.686455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:6b781275d47fdabb6c57421cecc362abd0c32bb3dae7c9eab6a099876083a806

Observation 82cf9000-87f7-4772-9ef3-a311d11e2867 · outbound

This paper cites Forty-first International Conference on Machine Learning , year=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling Forty-first International Conference on Machine Learning , year=

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.692440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:1bcca4569b4fa03f751c141f1152c026a1a5cbbdfbe75ae85f29c3cefc5adf06

Observation 9ee33657-ce3c-4cf3-8635-a6eb9076dd8b · outbound

This paper cites Advances in neural information processing systems , volume=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling Advances in neural information processing systems , volume=

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.698340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:75e52f71b033b07fb8682d1ed0a4a3b4735313293f9552c37c87f1712b4bac28

Observation 8a72521f-05bd-4a29-a90a-2e309bd8c03e · outbound

This paper cites International Conference on Machine Learning , pages=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling International Conference on Machine Learning , pages=

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.723513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:1a69be4548bb7881590999b5b251e323826ccf1f3642a715e868709250cd050c

Observation 6f114d7f-de38-47a1-8775-4bc5058c653f · outbound

This paper cites Forty-first International Conference on Machine Learning , year=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling Forty-first International Conference on Machine Learning , year=

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.837546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:c4f48190b5767d1af37d43410dd3016e1854bfa8d4e7f0e47d04ded8a74ba7e3

Observation 80c2eaba-9383-4a39-8f64-6f3bbb6e244a · outbound

This paper cites 2024 , booktitle=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling 2024 , booktitle=

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.840546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:48464dc82595dcc6fb62c5c5daaca223d257658ab2338b0bc6b5dc806ace3e59

Observation 0089b6c8-9fea-4e79-aeab-f0cc545abc88 · outbound

This paper cites Characterizing Structural Regularities of Labeled Data in Overparameterized Models.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling Characterizing Structural Regularities of Labeled Data in Overparameterized Models

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-05-25T05:25:23.251750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:ba6367e8b9063797e6eb3648cee0b926cb73167b5fee3d5f7d22be24c48ee591

Observation c01ba000-4ee8-4625-93c8-75a135bd2237 · outbound

This paper cites 2023 , eprint=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling 2023 , eprint=

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.826382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:702788fe46d5cd8fc060ca05d3157b0a010e6d7f761a0a6a8af5151469172ac4

Observation d6df5d30-fc6c-43d4-a583-f51c7f33e62c · outbound

This paper cites International Conference on Machine Learning , pages=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling International Conference on Machine Learning , pages=

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.829560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:e38f3aa2638110540e940bb7059b52b5b8e282f56d2abb701eb6c7a0d9e950d0

Observation b625ef86-149b-4daf-ae8a-c8d39b287368 · outbound

This paper cites International Conference on Machine Learning , pages=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling International Conference on Machine Learning , pages=

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.833073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:f0887b9f6e3e1d39e607bd48e2575147422fe29dceb75ec346379b64f86bc400

Observation fdef7d27-26dc-4190-805a-b8e06ae498a4 · outbound

This paper cites Advances in neural information processing systems , volume=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling Advances in neural information processing systems , volume=

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.850567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:cab7a9eca6e7e69b6801dae44ab7b594a8532c918f5e0bf78d9f2ee409ea0d8d

Observation 46ba9835-fd0e-44b8-bfef-273bb73c14bb · outbound

This paper cites Eleventh International Conference on Learning Representations , year=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling Eleventh International Conference on Learning Representations , year=

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.843999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:1aada87af60c21b0c3ad2ea54bdd73d6db79c84855c2b78087ca44ad0c0d2a73

Observation 64a90865-cecb-4107-87a8-165728370e95 · outbound

This paper cites Bartoldson and Bhavya Kailkhura and Atul Prakash , booktitle=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling Bartoldson and Bhavya Kailkhura and Atul Prakash , booktitle=

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.853274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:07649626e765f1fa1c577b61c4bc9891e54b92cc40e110886c6ad09611f45855

Observation 71d52ce2-7ccb-427d-810f-1a71346eb7f5 · outbound

This paper cites Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , pages=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , pages=

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.804439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:c42f97062f7c1237945fb9244629403ef5316b4bb6f7fbfb27433bff47f1cb2b

Observation ecf77e62-0e05-4917-87b8-df54809f9916 · outbound

This paper cites International Conference on Learning Representations , year=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling International Conference on Learning Representations , year=

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.801444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:613ae765393f27d2c028ae939ee71265514b869f6c81414258d0efba9d698c61

Observation e0601b79-0d25-4409-880e-b1261462d1c7 · outbound

This paper cites Advances in neural information processing systems , volume=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling Advances in neural information processing systems , volume=

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.798136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:6f222284a00a106a2cfc2f5648e4b8bdfc79d3d852f69b551c1075111a3858bc

Observation 499a6883-780d-4595-8bc4-fa1f461ba0f2 · outbound

This paper cites Exploring the Limits of Deep Image Clustering using Pretrained Models.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling Exploring the Limits of Deep Image Clustering using Pretrained Models

Reference 31

Resolution
verified exact
arxiv_id, observed 2026-05-25T05:25:23.257039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:af000540dbd97bd06a43e6ec3bd6bae27c18e43fd65cd3817a00e090f098be28

Observation 11621bc0-6bff-4707-b54f-6f209a5780a0 · outbound

This paper cites Transactions on Machine Learning Research , issn=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling Transactions on Machine Learning Research , issn=

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.807833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:5d523fce8699792d5fe7614dede6270f962f1ff431278c184c49f2eb9a4e2e13

Observation f8b028b0-7f42-4f30-a8ba-1ce201b9cfce · outbound

This paper cites 2026 , url=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling 2026 , url=

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.810819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:09dd6907f2238757e66b0f8dfe64be5ba65630b305929a746ad9fb57909bdec2

Observation b72e6763-681f-4bb9-9e52-ad3c5c09c290 · outbound

This paper cites an unresolved cited work.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling Unresolved cited work

Reference 34

Resolution
unresolved
raw_fallback, observed 2026-05-25T11:50:46.816899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:fe403591644d34e46eb8877fb5db75ee965fde67936add3f81504d1a98cd5d96

Observation 764de65b-7edb-4a93-ab92-5340144e79cd · outbound

This paper cites Advances in neural information processing systems , volume=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling Advances in neural information processing systems , volume=

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.788546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:97f2b7e9feed3fe767ab9f472662444a9c2345909a9e2f6ede965ad501d72e38

Observation 83c4931d-50a4-4519-b30d-133ef9504bc5 · outbound

This paper cites International Conference on Learning Representations , year=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling International Conference on Learning Representations , year=

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.791564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:17a73b54f7c105b9a2805de389c64936aa7bb511e1340fddf3c2c6480ad86f68

Observation ca8eaff5-7a43-4c9d-ab4e-838438f31631 · outbound

This paper cites International Conference on Machine Learning , pages=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling International Conference on Machine Learning , pages=

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.846960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:8be8c50821b30257f76d303991733157f2fd236847996d5a544a1ee4919cbe4a

Observation ac83caca-b641-4bbd-a5ba-575fc66cfed1 · outbound

This paper cites The Twelfth International Conference on Learning Representations , year=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling The Twelfth International Conference on Learning Representations , year=

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.779967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:d0dd59de9ffb8f382419943eb7a96e8565f58e08cee4fcb8fbc9a275c67fad9a

Observation 402b6c21-dc78-41b6-987f-3583ad12f268 · outbound

This paper cites The Thirteenth International Conference on Learning Representations , year=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling The Thirteenth International Conference on Learning Representations , year=

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.782757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:b8daf23b264d08ad3806811d70c9aef07560b269a3dd4dab0a55383ccb7f81f2

Observation 73564730-9211-4da0-b853-7fd42d35b21b · outbound

This paper cites The Twelfth International Conference on Learning Representations , year=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling The Twelfth International Conference on Learning Representations , year=

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.785509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:95165575c5c15db71247a0c0d471e13bd55de7af31979847ce52bb4c8f88143c

Observation f129b077-6c59-445f-93d8-8022a95a7b53 · outbound

This paper cites Workshop on challenges in representation learning, ICML , volume=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling Workshop on challenges in representation learning, ICML , volume=

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.794290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:fe8265c0cbe2323995a499361c6db5f343645f7c86b979d63139cad88091f728

Observation 7960e13a-b84f-4243-8e8a-3a96d0d11f40 · outbound

This paper cites Advances in neural information processing systems , volume=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling Advances in neural information processing systems , volume=

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.813905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:afa4c9a0844452a72105b553da05442e5663ba1cfbcd24ebf3064e54a24e51c4

Observation cab00001-80e2-44a4-9f14-ff7c1d5f93a7 · outbound

This paper cites Advances in neural information processing systems , volume=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling Advances in neural information processing systems , volume=

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.819871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:6da24ae7e3e1a85645ab0d9c4aa8fe62eea1e21ec8c3bca0665ef69b914f33bf

Observation 3d4ab60d-b45f-4eac-b040-ec05905a6ab3 · outbound

This paper cites Advances in neural information processing systems , volume=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling Advances in neural information processing systems , volume=

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.767805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:f1154f45a4bab6ee844df78742a7509abbbead4ce01b9515a98ae1f5aa5d6c82

Observation 36d8ec5a-8a8a-4444-85b4-8255aa2f8cb6 · outbound

This paper cites The Eleventh International Conference on Learning Representations , year=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling The Eleventh International Conference on Learning Representations , year=

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.773925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:00757d575a0e0ab9f68a583897f394e6b8e57ebe6b0378659b5baf58e192ee60

Observation c3fab435-f56d-441c-af38-2e4fa865ac1d · outbound

This paper cites an unresolved cited work.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling Unresolved cited work

Reference 46

Resolution
unresolved
raw_fallback, observed 2026-05-25T11:50:46.859349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:ffa0db62b3afd6d9d66c998fd55fb80894b24daaaa8a339f50f01c2917349b75

Observation db3dccad-ac9e-4343-8a70-7ec2556d5b37 · outbound

This paper cites The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.729204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:0b762a76e234bd7ddc72ba915edc3e755b2cc7355feba8488abaceefe50363ba

Observation 816e8c0b-f71e-4930-bed3-7249d16ac429 · outbound

This paper cites International conference on machine learning , pages=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling International conference on machine learning , pages=

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.732972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:f39103233764668966830de354dbbdc689fab04b0981b8d8a7d50d31d480b19d

Observation 3d4c1593-aa53-43f2-9040-e38aaf25eb50 · outbound

This paper cites Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.745986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:cd849d4695124146cafd249101e5ea8e6efe654acaa5db27554ae2c171c54fb4

Observation 6d98365d-1f1b-40d8-ab4b-6e7b753b1e49 · outbound

This paper cites International Conference on Machine Learning , pages=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling International Conference on Machine Learning , pages=

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.776742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:e444dee49ff8c44ac27f7185f7797b393e11d317e2141b63139780835af2ca11

Observation d5068fdd-1eda-4923-8dd8-c7f6688b4326 · outbound

This paper cites Proceedings of the European conference on computer vision (ECCV) , pages=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling Proceedings of the European conference on computer vision (ECCV) , pages=

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.823298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:95af0f43701607befa101ce72f5a1504d9566a3c8bf2c0f68f25146d1a641ec7

Observation 10245742-a782-47a3-9bc3-290ff44444b6 · outbound

This paper cites Advances in neural information processing systems , volume=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling Advances in neural information processing systems , volume=

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.862404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:5a18044277895f465b29103d6ac7544b707a2b0ee16a864517aed32668f2139c

Observation 76237bb7-5607-4834-9f19-023bed64413f · outbound

This paper cites Advances in neural information processing systems , volume=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling Advances in neural information processing systems , volume=

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.865708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:87e9d1fcbb144ef5fd031f4991b20ff88b078b9077a5cc662dd9e20e8fbfff77

Observation ca6d0909-2d9f-4fa8-8c28-e16dccb4aded · outbound

This paper cites International Conference on Learning Representations , year=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling International Conference on Learning Representations , year=

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.761592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:92fe603745f8902fec7f8fcb4a7a210b9733b23c5deaea43b1997f1c4517f8d3

Observation 9c3710d1-2299-478f-855b-42972ae67100 · outbound

This paper cites European conference on computer vision , pages=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling European conference on computer vision , pages=

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.764419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:f8ecdf87122a27e9676c3aec9415f515838b4da6293e559317f2c8c7ca6b43e3

Observation 43fb4f90-bfad-457c-8763-bf47b5d06a26 · outbound

This paper cites Advances in neural information processing systems , volume=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling Advances in neural information processing systems , volume=

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.856363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:f8acb501b8afd7067bd60cd3bd7d74fb4c3efa3d0eb977c05e1eab6560bfe30f

Observation b7f69828-fe47-4ee6-bab8-93509eeea194 · outbound

This paper cites Proceedings of the IEEE/CVF international conference on computer vision , pages=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling Proceedings of the IEEE/CVF international conference on computer vision , pages=

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.749386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:9e58f3d373ad62e86c7fab7d795f3302bfb05dc661e4751454c1e5b6ee98a821

Observation 963ad27e-988a-4e50-a3a2-532f529bbca3 · outbound

This paper cites Proceedings of the AAAI conference on artificial intelligence , volume=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling Proceedings of the AAAI conference on artificial intelligence , volume=

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.751973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:573f5ee17325e8c820aeff25f29e19882b41e3604d27e7c502c72d18543191aa

Observation de8af512-2974-4bf0-a106-a9fb6dd0aaa6 · outbound

This paper cites International conference on machine learning , pages=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling International conference on machine learning , pages=

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.758313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:dc8b0d03e6804b96d1975319e72b5bb2c3c6e5b728cc318da1918018b2dd6dba

Observation daf2354a-680c-4c3e-bda1-c1d1cbd7e5f2 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling DINOv2: Learning Robust Visual Features without Supervision

Reference 60

Resolution
metadata mismatch
local_arxiv, observed 2026-05-25T05:25:23.265428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:75205521bc3e28302a72c38e3c002f498a549189b409be23674c7dfe20be7d29

Observation 283d6175-f4ae-4928-8eaa-1b4ed7f4863e · outbound

This paper cites European conference on computer vision , pages=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling European conference on computer vision , pages=

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.739532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:0fdf76a173098529bccc63c7c98495d9038410c51f109f94771a19f53bdbb69d

Observation 0a57dc36-d0f7-4439-aba5-96d726f9ae81 · outbound

This paper cites 2010 IEEE computer society conference on computer vision and pattern recognition , pages=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling 2010 IEEE computer society conference on computer vision and pattern recognition , pages=

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.736218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:8b6a92eae0818d15c4b94e8801922ef3d091597161fafb272ad17c371f2f6c74

Observation fa22d007-77aa-4b32-84a6-f9fa7e31ab7d · outbound

This paper cites 2004 conference on computer vision and pattern recognition workshop , pages=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling 2004 conference on computer vision and pattern recognition workshop , pages=

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.742452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:4929e48d1d1ea8fdeba5b4f24a4a2d38e86b48da67ed513439caa68e0b37324b

Observation 0fb04b15-b6d1-495e-a8c4-17d62c0cc333 · outbound

This paper cites 2009 , url =.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling 2009 , url =

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.754586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:b4c6d0abe26563263cc6c7ea7906ecf65c047267b54a007af2adc9a9ddc00784

Observation 85e13023-6b30-4663-b67b-b65c2350f208 · outbound

This paper cites 2009 IEEE conference on computer vision and pattern recognition , pages=.

Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling 2009 IEEE conference on computer vision and pattern recognition , pages=

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T11:50:46.771279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-25T05:24:53.628214Z digest=sha256:042c355e568bbce8b560b249131c4f46d71607e82ca5331a11274aae99c4e981

Pith citing papers

No inbound Pith citation observations are available.